Summary: Simultaneous localization and mapping (SLAM) has received quite a lot of attention in the last decades because of its relevance for many applications centered on a mobile observer, such as service robotics and intelligent transportation systems. This paper focuses on the use of recursive Bayesian filtering, as implemented by the extendend Kalman filter (EKF), to face the visual SLAM problem, i.e., when using data from visual sources. In monocular SLAM, which uses a single camera as the unique source of information, it is not possible to directly estimate the depth of a feature from a single image. To handle the severely non-normal distribution representing such uncertainty, inverse parametrizations were developed, capable to deal with such uncertainty and still relying on Gaussian variables. In the paper, after an introduction to EKF-SLAM, we provide a review of different inverse parametrizations, and we introduce a novel proposal, the framed inverse depth (FID) parametrization, which, in terms of consistency, performs similarly to the state of the art monocular SLAM parametrizations, but at a reduced computational cost. All these parametrizations can be used in a stereo and multi-camera setting, too. An extensive analysis is presented for both the monocular and the stereo SLAM, for a simulated environment widely used in the literature as well as on a widely used real dataset.